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                <h1 id="（个人向）人脸识别，图像处理内容入门学习。"><a href="#（个人向）人脸识别，图像处理内容入门学习。" class="headerlink" title="（个人向）人脸识别，图像处理内容入门学习。"></a>（个人向）人脸识别，图像处理内容入门学习。</h1><h2 id="背景及意义"><a href="#背景及意义" class="headerlink" title="背景及意义"></a>背景及意义</h2><p>人脸识别，是基于人的脸部特征信息进行身份识别的一种生物识别技术。用摄像机或摄像头采集含有人脸的图像或视频流，并自动在图像中检测和跟踪人脸，利用人脸固有的特征信息进而对检测到的人脸进行脸部识别的一系列相关技术，通常也叫做人像识别、面部识别。是模式识别和人工智能最广泛的应用之一，通常也叫做人像识别、面部识别。</p>
<h2 id="人脸识别过程简述"><a href="#人脸识别过程简述" class="headerlink" title="人脸识别过程简述"></a>人脸识别过程简述</h2><p><img src="/2020/12/09/ren-lian-shi-bie-ru-men-nei-rong-jian-jie/p1.png" alt="人脸识别流程图"></p>
<ol>
<li>人脸图像采集及检测</li>
</ol>
<p>人脸图像采集,不同的人脸图像都能通过图像采集设备采集下来，比如静态的图像、动态图像、不同的位置、不同表情等方面都可以得到很好的采集。人脸检测，在实际中主要用于人脸识别的预处理，即就是在图像中准确标出人脸的位置和大小，人脸图像中包含的模式特征十分丰富，如直方图特征、颜色特征、模板特征、结构特征等。人脸检测就是把这其中有用的信息挑出来，并利用这些特征实现人脸检测。</p>
<ol start="2">
<li>人脸图像预处理</li>
</ol>
<p>这是以人脸检测结果为基础的，对图像进行处理并最终服务于特征提取的过程。系统获取的原始图像由于受到各种条件的限制和随机干扰，往往不能直接使用，必须在图像处理的早期阶段对它进行灰度校正、噪声过滤等图像预处理。对于人脸图像而言，其预处理过程主要包括人脸图像的光线补偿、灰度变换、直方图均衡化、归一化、几何校正、滤波以及锐化等。</p>
<ol start="3">
<li>人脸图像特征提取</li>
</ol>
<p>人脸图像特征提取：人脸识别系统可使用的特征通常分为视觉特征、像素统计特征、人脸图像变换系数特征、人脸图像代数特征等。人脸特征提取就是针对人脸的某些特征进行的。人脸特征提取，也称人脸表征，它是对人脸进行特征建模的过程。人脸特征提取的方法归纳起来分为两大类：一种是基于知识的表征方法；另外一种是基于代数特征或统计学习的表征方法。</p>
<ol start="4">
<li>人脸图像匹配与识别</li>
</ol>
<p>人脸图像匹配与识别：提取的人脸图像的特征数据与数据库中存储的特征模板进行搜索匹配，通过设定一个阈值，当相似度超过这一阈值，则把匹配得到的结果输出。人脸识别就是将待识别的人脸特征与已得到的人脸特征模板进行比较，根据相似程度对人脸的身份信息进行判断。这一过程又分为两类：一类是确认，是一对一进行图像比较的过程，另一类是辨认，是一对多进行图像匹配对比的过程。</p>
<h2 id="各个部分的简单介绍"><a href="#各个部分的简单介绍" class="headerlink" title="各个部分的简单介绍"></a>各个部分的简单介绍</h2><p><strong>每个部分只单单做简单介绍，可能只会提及少数常用的方法。</strong></p>
<h3 id="人脸图像的预处理"><a href="#人脸图像的预处理" class="headerlink" title="人脸图像的预处理"></a>人脸图像的预处理</h3><p>人脸识别的图像一般都是通过摄像机、照相机等摄录设备进行采集的，但由于采集的过程中的周围光照环境，强度的不同，所采用的照相设备性能有所不同不同，而导致所采集到的图像存在噪声，而且摄像角度和摄像距离的不同还会导致所得图像中人脸的大小，位置和角度不统一。为了保证识别系统中的对人脸图像的识别具有相同的质量，预处理就成为了人脸识别系统中必不可少的一个步骤。下面介绍几种经常用到的图像预处理方法。</p>
<p><strong>灰度变换</strong></p>
<p>灰度变换指的是根据需要的目标条件按一定变换关系逐点改变源图像中每一个像素的灰度值的一种方法。目的是为了改善画质，使图像的显示效果更加清晰。图像的灰度变换处理是图像增强处理技术中的一种非常基础、直接的空间域图像处理方法，也是图像数字化软件和图像显示软件的一个重要组成部分。</p>
<p>在人脸识别系统中我们所采集到的图像一般是彩色图像，但由于彩色图像中包含较大的数据量和信息量，所以直接对彩色图像进行处理不仅复杂而且耗时。而灰色图像虽然包含数据量较小，但包含的有用信息量却很大，更适合于人脸识别系统的应用。也因此，把彩色图像灰度化是人脸图像预处理的第一步。常用的灰度变换方法有线性变换，分段线性变换和非线性变换方法。</p>
<p>灰度化常用的经验公式: (其中R G B分别代表彩色图像RGB对应的值，grey则为对应的灰度值)</p>
<hr>
<p><strong>gray=0.39×R＋0.5×G＋0.11×B</strong></p>
<hr>
<p><img src="/2020/12/09/ren-lian-shi-bie-ru-men-nei-rong-jian-jie/p2.png" alt="灰度处理流程"></p>
<p><strong>下面是灰度变换的方法(r为输入灰度值，s为输出灰度值)</strong></p>
<ol>
<li><p>​    线性变换-较多的是图像反转，图像反转得到的是图像的负片，能够有效的增强在图像暗区域的白色或者灰色细节。</p>
<hr>
<p><strong>S = 255-r</strong></p>
<hr>
</li>
</ol>
<ol start="2">
<li><p>对数变换和反对数变换（其中c是一个常数）将源图像中<strong>范</strong>围较窄的低灰度值映射到范围较宽的灰度区间，同时将范围较宽的高灰度值区间映射为较窄的灰度区间，从而扩展了暗像素的值，压缩了高灰度的值，能够对图像中低灰度细节进行增强。</p>
<hr>
<p><strong>S = clog(1+r)</strong></p>
<hr>
</li>
</ol>
<p><img src="/2020/12/09/ren-lian-shi-bie-ru-men-nei-rong-jian-jie/p3.png" alt="对数和反对数函数对应图像"></p>
<ol start="3">
<li><p>幂律变换（其中c和γ为正常数[当γ&lt;1γ&lt;1时，γ的值越小，对图像低灰度值的扩展越明显；当γ&gt;1γ&gt;1时，γ的值越大，对图像高灰度值部分的扩展越明显。这样就能够显示更多的图像的低灰度或者高灰度细节。</p>
<hr>
<p><strong>S = （cr）γ次方</strong></p>
<hr>
</li>
</ol>
<p><img src="/2020/12/09/ren-lian-shi-bie-ru-men-nei-rong-jian-jie/p4.png" alt="幂律变换对应图像"></p>
<p><strong>几何矫正（具体内容略，只做简述）</strong></p>
<p>几何校正是用来解决角度问题，主要包括：</p>
<ul>
<li>图像的大小矫正。</li>
<li>图像的平移。</li>
<li>图像的翻转。</li>
<li>图像的旋转。</li>
</ul>
<p>常用的方法有两种：</p>
<ul>
<li>直接几何校正。</li>
<li>间接法几何校正。</li>
</ul>
<p><strong>图像滤波，对图像噪点进行处理</strong></p>
<p>由于人脸图像的采集环境往往是不理想的，比如存在光照强度的变换、采集设备性能的差异等等都会使采集到的图像中存在噪声，从而影响到采集到的图像质量。为了提升图像的质量水平，减少噪声对后期处理的不良影响，我们需要将图像中的噪声去除。目前常用的图像去噪方法可以分为两类：一类为对图像的全局处理，该类滤波需要事先对信号及噪声的统计模型有所了解，如Wiener滤波、卡尔曼滤波等；另一类为采用局部算子进行的滤波，如中值滤波、均值滤波等。</p>
<ol>
<li>  均值滤波。</li>
</ol>
<p>作为最简单的线性滤波器之一，其先对图像中目标像素进行局部平均,再把原来的像素值替换成局部平均值。邻域的大小对滤波效果也有很大的影响，邻域窗口的尺寸越大，滤波效果也会更好，但同时也会造成大量图像细节信息的损失，即图像将会变得模糊。</p>
<p><img src="/2020/12/09/ren-lian-shi-bie-ru-men-nei-rong-jian-jie/p5.png" alt="均值滤波公式"></p>
<ol start="2">
<li>中值滤波</li>
</ol>
<p>中值滤波也是一种常见的非线性滤波技术。该方法主要思想是把每一个像素点的灰度值设置为该像素点邻域内的所有像素点灰度值的中值，由于该方法不依赖于邻域中与典型值差别很大的值，所以在去除椒盐噪声和脉冲噪声的同时该方法还能够很好的保留人脸图像的边缘细节信息。</p>
<p>在图像中去3*3的矩阵，里面有9个像素点，我们将9个像素进行排序，最后将这个矩阵的中心点赋值为这九个像素的中值。如图所示。</p>
<p><img src="/2020/12/09/ren-lian-shi-bie-ru-men-nei-rong-jian-jie/p6.png" alt="中值滤波"></p>
<p><strong>图像特征提取方法</strong></p>
<ol>
<li><p>SIFT（尺度不变特征变换）</p>
<p>1.1 尺度空间极值检测：搜索所有尺度上的图像位置。通过高斯微分函数来识别潜在的对于尺度和旋转不变的兴趣点。</p>
<p>1.2 关键点定位：在每个候选的位置上，通过一个拟合精细的模型来确定位置和尺度。关键点的选择依据于它们的稳定程度。</p>
<p>1.3 方向确定：基于图像局部的梯度方向，分配给每个关键点位置一个或多个方向。所有后面的对图像数据的操作都相对于关键点的方向、尺度和位置进行变换，从而提供对于这些变换的不变性。</p>
<p>1.4 关键点描述：在每个关键点周围的邻域内，在选定的尺度上测量图像局部的梯度。这些梯度被变换成一种表示，这种表示允许比较大的局部形状的变形和光照变化。</p>
</li>
</ol>
<p>经过上述的步骤，尺度变化以及旋转等几何变换的影响就都能够被去除，如果还想再去除光照变化对其产生的影响，可以对特征点描述子进行长度归一化处理</p>
<ol start="2">
<li>PCA-SIFT（主成分分析法结合SIFT）</li>
<li> GLOH（方向梯度直方图）</li>
</ol>
<p><strong>本次内容对于特征提取的方法不做讲解，因为特征提取的方法内容比较多，所以之后会对于不同的特征提取方法单独写篇文章进行具体探讨，这次只提出。</strong></p>
<p><strong>最后的特征匹配和验证</strong></p>
<p>提取的人脸图像的特征数据与数据库中存储的特征模板进行搜索匹配，通过设定一个阈值，当相似度超过这一阈值，则把匹配得到的结果输出。人脸识别就是将待识别的人脸特征与已得到的人脸特征模板进行比较，根据相似程度对人脸的身份信息进行判断。这一过程又分为两类：一类是确认，是一对一进行图像比较的过程，另一类是辨认，是一对多进行图像匹配对比的过程。</p>
<p>支持向量机（Support Vector Machine, SVM）是一类按<a target="_blank" rel="noopener" href="https://baike.baidu.com/item/%E7%9B%91%E7%9D%A3%E5%AD%A6%E4%B9%A0/9820109">监督学习</a>（supervised learning）方式对数据进行<a target="_blank" rel="noopener" href="https://baike.baidu.com/item/%E4%BA%8C%E5%85%83%E5%88%86%E7%B1%BB/15635322">二元分类</a>的广义线性分类器（generalized linear classifier），其<a target="_blank" rel="noopener" href="https://baike.baidu.com/item/%E5%86%B3%E7%AD%96%E8%BE%B9%E7%95%8C/22778546">决策边界</a>是对学习样本求解的最大边距超平面（maximum-margin hyperplane） 。</p>
<p>SVM使用铰链损失函数（hinge loss）计算经验风险（empirical risk）并在求解系统中加入了正则化项以优化结构风险（structural risk），是一个具有稀疏性和稳健性的分类器  。SVM可以通过<a target="_blank" rel="noopener" href="https://baike.baidu.com/item/%E6%A0%B8%E6%96%B9%E6%B3%95/1683712">核方法</a>（kernel method）进行非线性分类，是常见的核学习（kernel learning）方法之一  。</p>
<p>还有就是最常用的神经网络方法，但是神经网络内容也比较多，后面想要单独对神经网络的不同模型进行单独探讨，本次只提一下。</p>
<h2 id="由于图像处理，人脸识别内容比较多且庞杂，所以本次只当作对于该领域学习的一个开端，只会也会伴随着我自己的学习不断深入之后继续写一些自己思考的内容以供交流和记录。水平有限，内容不一定正确，如有错误请多多指正。-如果内容对您有帮助或者对此感兴趣请继续关注哦！！！"><a href="#由于图像处理，人脸识别内容比较多且庞杂，所以本次只当作对于该领域学习的一个开端，只会也会伴随着我自己的学习不断深入之后继续写一些自己思考的内容以供交流和记录。水平有限，内容不一定正确，如有错误请多多指正。-如果内容对您有帮助或者对此感兴趣请继续关注哦！！！" class="headerlink" title="由于图像处理，人脸识别内容比较多且庞杂，所以本次只当作对于该领域学习的一个开端，只会也会伴随着我自己的学习不断深入之后继续写一些自己思考的内容以供交流和记录。水平有限，内容不一定正确，如有错误请多多指正。             如果内容对您有帮助或者对此感兴趣请继续关注哦！！！"></a>由于图像处理，人脸识别内容比较多且庞杂，所以本次只当作对于该领域学习的一个开端，只会也会伴随着我自己的学习不断深入之后继续写一些自己思考的内容以供交流和记录。水平有限，内容不一定正确，如有错误请多多指正。             如果内容对您有帮助或者对此感兴趣请继续关注哦！！！</h2>
                
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